Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

On the Stabilization of Finite Element Approximations of the Stokes Equations

Abstract: Consider finite element approximation of the Stokes equations. We present a systematic way of stabilizing it by adding bubble functions to the discrete velocity field. Another way of stabilization is also presented where the finite element spaces are kept unchanged but the discrete incompressibility condition is modified instead.

A survey of Bayesian predictive methods for model assessment, selection and comparison

Abstract: To date, several methods exist in the statistical literature for model assessment, which purport themselves specifically as Bayesian predictive methods. The decision theoretic assumptions on which these methods are based are not always clearly stated in the original articles, however. The aim of this survey is to provide a unified review of Bayesian predictive model assessment and selection methods, and of methods closely related to them. We review the various assumptions that are made in this context and discuss the connections between different approaches, with an emphasis on how each method approximates the expected utility of using a Bayesian model for the purpose of predicting future data.

Performance of serial acquirers: toward an acquisition program perspective

Abstract: Based on an analysis of the most active acquirers in seven industry sectors in the United States in the 1990s, we find that both a high rate of acquisitions and a high variability of the rate are negatively related to performance. An acquirer's size, the scope of its acquisition program, and acquisition experience moderate the relationship by weakening the negative effects. Our findings contribute to an improved understanding of acquisition capabilities and program-level acquisition performance, thereby adding to an emerging stream of research that is building an acquisition program perspective.

Influence of emulsion and powder size on the stability of encapsulated d-limonene by spray drying

Abstract: The microencapsulation of d -limonene by spray drying was investigated with respect to the effects of emulsion droplet size, powder particle size, as well as to the effects of various kinds of matrices (gum arabic, maltodextrin, and modified starch) on its stability. It was realized by studying release characteristics and oxidative stability during storage. The release and the oxidation decreased deeply with an increase in powder and emulsion particle size for gum arabic and maltodextrin materials. Further, the distributions of emulsion size in the powder showed an increase in the fraction of large emulsion droplets and changed to a bimodal distribution. However, the modified starch HI-CAP 100 showed a higher stability of encapsulated d -limonene than the others. The influence of powder and emulsion size on its encapsulated flavor as well as the change in the emulsion size during storage could not be observed. Industrial Relevance Spray drying is a common and useful unit operation for microencapsulation of food ingredients. Data on emulsion droplet size and on powder size on product stability provide conflicting results which makes a systematic study regarding these factors highly relevant. The data suggest that an optimal size of flavour powder should be selected for high retention during spray drying, stability during storage and for the ability to control release of flavour.